diff --git a/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py b/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py
index aa6146620ead..24c990498297 100644
--- a/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py
+++ b/tensorrt_llm/_torch/modules/mamba/gdn_mixer.py
@@ -890,14 +890,19 @@ def forward(
 
         state_indices_p, state_indices_d = torch.split(state_indices, batch_split_size)
         if num_prefills > 0:
-            # PyExecutor guarantees prefill requests are placed before decode requests
-            has_initial_states_p = has_initial_states[:num_prefills]
-            ssm_states[state_indices_p[~has_initial_states_p]] = torch.zeros(
-                (), dtype=ssm_states.dtype, device=ssm_states.device
-            )
-            conv_states[state_indices_p[~has_initial_states_p]] = torch.zeros(
-                (), dtype=conv_states.dtype, device=conv_states.device
-            )
+            if not mamba_metadata.use_initial_states:
+                # All prefills are fresh — zero every slot unconditionally.
+                for state in (ssm_states, conv_states):
+                    state[state_indices_p] = 0
+            else:
+                # Use torch.where so the output shape is data-independent;
+                # boolean-mask indexing on a CUDA tensor would force a CPU sync.
+                # PyExecutor guarantees prefill requests are placed before decode requests.
+                has_initial_states_p = has_initial_states[:num_prefills]
+                for state in (ssm_states, conv_states):
+                    kept = state[state_indices_p]
+                    mask = has_initial_states_p.view(-1, *([1] * (kept.ndim - 1)))
+                    state.index_copy_(0, state_indices_p, torch.where(mask, kept, 0))
 
         is_target_verify = (
             num_decodes > 0
diff --git a/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py b/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
index 7f1af74d4c83..d2e37cc12dff 100644
--- a/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
+++ b/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
@@ -1676,6 +1676,16 @@ def add_dummy_requests(
         num_extra_decoding_steps: int = 0,
         draft_kv_cache_manager: Optional[KVCacheManager] = None,
     ) -> List[LlmRequest]:
+        # The caller's get_num_free_blocks check sees only the full-attention
+        # window, but the unified C++ pool also holds the recurrent-states
+        # window used by mamba layers. If a CUDA-graph warmup batch exceeds
+        # that smaller window, add_sequence_batch raises "No free block found"
+        # from C++ and deadlocks ranks already past the collective. Return
+        # None so the caller skips this batch (the documented contract).
+        per_window_free = self.impl.get_kv_cache_stats(
+        ).num_free_blocks_per_window_size
+        if per_window_free and len(request_ids) > min(per_window_free.values()):
+            return None
         requests = super().add_dummy_requests(
             request_ids=request_ids,
             token_nums=token_nums,
diff --git a/tensorrt_llm/_torch/pyexecutor/model_engine.py b/tensorrt_llm/_torch/pyexecutor/model_engine.py
index 4e5cac179c02..0a9c8de66506 100644
--- a/tensorrt_llm/_torch/pyexecutor/model_engine.py
+++ b/tensorrt_llm/_torch/pyexecutor/model_engine.py
@@ -1128,6 +1128,22 @@ def _run_attention_warmup(self,
         if not issubclass(self.attn_backend.Metadata, TrtllmAttentionMetadata):
             return
 
+        # The C++ TRTLLM-Gen FMHA JIT warmup enumerates a (batchSize x seqLenKv)
+        # cartesian grid sized by engine maxima. For long-context configs such
+        # as Qwen3-Next-80B-A3B-Thinking tp4ep4 (max_batch_size=2048,
+        # max_seq_len=262144), the densified grid pushes warmup TMA descriptor
+        # shapes past the flashinfer 2^32 limit and hangs engine startup. Skip
+        # the warmup whenever the maxima product is too large; any kernel not
+        # pre-warmed JIT-compiles lazily on first request, which is correct
+        # (just slower for that one request). The threshold matches the
+        # pre-PR #15305 effective grid size.
+        if self.batch_size * self.max_seq_len > 256 * 16384:
+            logger.info(
+                f"Skipping TRTLLM-Gen FMHA JIT warmup: engine config "
+                f"(max_batch_size={self.batch_size}, max_seq_len={self.max_seq_len}) "
+                f"would produce too many warmup grid points")
+            return
+
         @contextlib.contextmanager
         def trtllm_gen_fmha_jit_warmup():
             previous = self._trtllm_gen_jit_warmup